THE RELATIOSHIP BETWEE EARIGS MAAGEMET AD

ETERPRISE RISK MAAGEMET: THE EFFECT OF ERM COMMITTEE

ABSTRACT

This paper provides initial evidence on the relation between Enterprise Risk Management (ERM) and earnings management (EM). Using the establishment of an ERM management committee that integrates into a firm’s management structure and complements a traditional board risk committee as our proxy, we first provide some statistically significant evidence that earnings management and earnings volatility are among the factors that determine whether a firm adopts ERM as a holistic risk management framework. We then document how the existence of an ERM committee affects based (AEM) and transaction-based (TEM) earnings management and earnings properties in subsequent periods. We find that an ERM committee correlates negatively with aggressiveness, smoothing and earnings volatility, but positively with firm performance as measured by earnings level. Lastly, we provide additional evidence that firms with relatively high earnings volatility benefit relatively more from the EM-mitigating effect of ERM.

Keywords: Transactions earnings management, Accrual earnings management, Enterprise Risk Management, Earnings volatility

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1. ITRODUCTIO

This paper provides initial evidence on the relation between Enterprise Risk Management

(ERM) and earnings management (EM). We identify firms that have established ERM

committees and document that these firms display less earnings smoothing and earnings

aggressiveness in subsequent periods than do the firms in a matched sample that do not have

such committee. We also provide some evidence that these firms show earnings with a lower

volatility and a higher level in subsequent periods than the matched firms. These findings are

of interest to users including regulators and investors who try to assess the

quality of firms. By newly documenting a link between ERM and earnings

management, our paper opens several avenues for future research.

Enterprise Risk Management (ERM) is a conceptual construct that comprises the

management systems of companies to holistically and strategically manage their exposure to

all kinds of value-relevant risks. Earnings management refers to the managerial practice “to

alter financial reports to either mislead some stakeholders about the underlying economic performance … or to influence contractual outcomes” (Healy and Wahlen 1999) by means of

manipulating accounting - accrual-based earnings management (AEM) – and real business activities - transaction-based manipulations (TEM). ERM and earnings management

each has individually attracted significant research interest in the accounting literature.

However, the possible interrelation between them has not yet been researched and is therefore

not yet well understood.

2 Our motivation to link ERM and earnings management is that they share some common practices and goals, although for different reasons and with different long-term effects on accounting earnings and firm value. For example, they both influence managers’ selection and optimization of business transactions and the magnitude and timing of accounting accruals. They both also target similar outcomes for accounting earnings, for example in form of a higher short-term level or a smoother stream over time. We intend to disentangle these perceived commonalities and the consequent interrelation between ERM and earnings management. Specifically, we ask whether establishing an ERM management committee reduces earnings management, both AEM and TEM, and results in lower earnings volatility and a higher earnings level.

The remaining sections of this paper review the literature (Section 2), develop the hypotheses

(Section 3), describe the sample selection and variable definition (Section 4), lay out the methodology (Section 5), discuss the results (Section 6), and present our conclusions and their implications for further research (Section 7).

2. ETERPRISE RISK MAAGEMET AD EARIGS MAAGEMET

ERM has emerged as a popular corporate practice, with more and more firms preferring its

“holistic” approach over the traditional “silo-based” approach to risk management (e.g.,

Gates and Hexter 2005). Other reasons for its popularity are, for example, the increased demand for risk management by investors, regulators, stock markets, and rating agencies.

Some studies have also found that highly leveraged firms are more inclined to adopt ERM

3 than are lightly leveraged firms (Liebenberg and Hoyt 2003). Defined as “the discipline by which an organization assesses, controls, exploits, finances, and monitors risks from all sources” , ERM has the overarching goal of managing corporate risks firm-wide and with a strategic perspective “for the purpose of increasing the organization’s short- and long term value to its stakeholders” (Casualty Actuarial Society Committee on Enterprise Risk

Management 2003, p. 8, as quoted in Gordon, Loeb and Tseng (2009)). Arguments in favor

of ERM working toward this value objective have referred to, for example, a reduction in

expected related to tax payments, financial distress, underinvestment, asymmetric

information, and risk mitigation for non-diversified stakeholders (e.g. Meulbroek 2002).

Proponents of ERM claim that ERM is designed to enhance shareholder value; however, portfolio theory suggests that costly ERM implementation would be unwelcome by

shareholders who can use less costly diversification to eliminate idiosyncratic risk.

Accordingly, previous research has tested whether ERM actually achieves to increase firm

value 1(Hoyt & Liebenberg, 2011) and if so, by which means. While constrained by some difficulties to identify and develop an adequate empirical measure for ERM, studies have documented some support that ERM has multiple benefits for fundamental business activities and financial outcomes. For example, ERM has been found to assist managerial decision- making by making risks more explicit and transparent, foster cooperation and integration across different functions and divisions (Kleffner, Lee, & McGannon, 2003),serve to generate synergies between risk management activities (Miccolis and Shah, 2000; Cumming and

Hirtle, 2001; Lam, 2001; Meulbroek, 2002), safeguard the firm’s reputation as a driver of future performance (Fombrun, Gardberg, & Barnett, 2000), achieve strategic, operational, reporting, and compliance objectives (Gordon, Loeb, & Tseng, 2009),increase firm value

1There is a large academic literature that investigates how firm value depends on total risk. For a review of that literature, see René Stulz, Risk Management and Derivatives , Southwestern Publishing, 2002.

4 (McShane, Nair, & Rustambekov, 2011), translate into excess stock market returns (Gordon,

Loeb, & Tseng, 2009), reduce stock price volatility, lower the -of-capital, or increase capital efficiency (Beasley et al., 2008, Hoyt and Liebenberg, 2011, Miccolis and Shah, 2000;

Cumming and Hirtle, 2001; Lam, 2003; Meulbroek, 2002). The tenor of these findings complements that of other studies providing evidence that individual practices to manage risk associate positively with firm value, for example hedging with derivatives (Bartram, Brown,

& Conrad, 2009; Carter, Rogers & Simkins, 2006; Graham & Rogers, 2002; Nelson, Moffitt,

& Affleck-Graves, 2005). If such individual, “silo-based” risk management practices each already associate positively with firm value, then it appears reasonable to expect that integrating these practices and managing them “holistically” with a portfolio approach would associate positively with firm value, too.

Some few papers have provided initial evidence that ERM might positively associate with value-relevant accounting performance outcomes such as earnings levels and volatility, confirming Lam’s prediction that ERM would allow firms to “produce more consistent

business results” (Lam, 2003).2 According to these papers, ERM associates with higher return-on- (ROA) (Hoyt & Liebenberg, 2011 ); (Baxter, Bedard, Hoitash, & Yezegel,

2012) and lower earnings volatility (Liebenberg & Hoyt, 2003). However, another study

finds little evidence for changes in earnings level or volatility after the appointment of a

CRO, which is one possible empirical proxy for a firm initiating ERM (Pagach & Warr,

2010).

This last set of consequences of ERM for accounting earnings – level and volatility –

overlaps with some of the consequences of earnings management. Earnings management is

2The benefits of reducing earnings volatility include increasing managerial compensation and wealth, reducing corporate income tax, reducing the cost of , avoiding underinvestment and earnings surprises, and mitigating volatility caused by low diversification (Barton, 2001).

5 the managerial practice to opportunistically modify reported short-term accounting performance outcomes without positively affecting fundamental firm value. It occurs when managers use their judgment and discretion “in financial reporting and in structuring transactions to alter financial reports to either mislead some stakeholders about the underlying economic performance of the company or to influence contractual outcomes that depend on reported accounting practices” (Healy and Wahlen, 1999).

Managers might refer to two different instruments to manage earnings. On the one hand, there is transaction-based earnings management (TEM), the manipulation of earnings through real business activities that achieves a reported economic performance that deviate from the performance that would be warranted by normal operational practices. Studies that directly examine TEM have concentrated mostly on price discounts, acceleration of sales, alterations in shipment schedules, scale-backs in research and development (R&D), and delays of maintenance (Healy and Wahlen, 1999; Fudenberg and Tirole, 1995), and Dechow and

Skinner (2000) Roychowdhury (2006). On the other hand, there is accounting (accrual-based) earnings management (AEM), the manipulation of earnings through the opportunistic usage of discretion in applying accounting policies. With AEM, managers do not intervene in real economic activities but make accrual decisions that they would otherwise not make was it not merely for achieving some desired accounting outcomes. Following Jones (1991), a large body of literature has developed and applied various empirical strategies and models to detect and measure AEM; the most popular models are summarized in Table A1 (annex). Common to these strategies is that they are constrained by the noisiness of available proxies and by the fact that earnings management is driven by unobservable intentions and done within accounting regulations.

6 The short-term accounting objectives of earnings management may be diverse. On the one hand, they may refer to meeting or beating certain benchmarks like earnings break-even

(Burgstahler and Dichev, 1997), analyst forecasts (Das and Zhang, 2003), or other short-term reporting goals (Chamberlain and Magliolo, 1995). On the other hand, they may refer to the properties of earnings like increasing their level (DeAngelo, 1988; Pourciau, 1993) or

reducing their volatility (Lambert, 1984; Demski, 1998; Kirschenheiter and Melumad, 2002);

following (Leuz, Nanda, & Wysocki, 2003), we will refer to these two latter phenomena as

earnings aggressiveness and earnings smoothness.

These two latter consequences of earnings management are congruent with the earnings

consequences of ERM, providing motivation to link the two to each other in this study. If

ERM and earnings management pursue similar objectives for accounting earnings, then they

might complement or substitute each other. Prior evidence on this possible interrelation is

somewhat scant, fragmented, inconclusive, and geared towards the board oversight over risk

and earnings management. Barton (2001) finds that managing financial risks, proxied by

derivatives’ notional amounts, partially substitutes for AEM. He also document that the

magnitude of discretionary accruals relates to risk-management-related benefits such as

increasing managerial compensation and wealth, reduced corporate income taxes and debt

financing costs, preempted underinvestment and earnings surprises, and mitigated volatility

caused by low diversification – all dimensions that are arguably also within the objective set

of ERM (Meulbroek 2002). Davidson et al. (2005) document for a sample of Australian firms

that some governance mechanisms primarily targeting compliance risks – a majority of non-

executive directors on the board and its committee – are significantly associated with a

lower likelihood of AEM while some control mechanisms – the voluntary establishment of an

function and the choice of auditor – are not. Others similarly provide evidence

7 that the composition of the board and its audit committee, i.e. two governance institutions tasked with risk oversight, deter earnings management by constraining the managerial propensity to engage in it (Xie, Davidson III, & DaDalt, 2003; Bedard & Johnstone, 2004;

Klein, 2006; Davidson, Goodwin-Stewart, & Kent, 2005). However, we are not aware of any study that directly investigates the interrelation between managerial activities of risk management and managerial activities of earnings management.

In our analyses, we take the constitution of an ERM management committee as our proxy of

ERM. This choice is based on our understanding that constituting such a committee is a stronger signal of a comprehensive, firm-wide ERM than are generically describing ERM initiatives (e.g. Gordon, Loeb and Tseng 2009; Hoyt and Liebenberg 2011) or appointing a

CRO (Liebenberg and Hoyt (2003). In a first step we find that leverage, size, risk and firm performance are the main determinants for companies to implement an ERM management

committee. In a second step, we find evidence of a generally negative effect of ERM on

earnings management and its instruments TEM and AEM in subsequent periods. A more

rigorous “matching on observables” analysis confirms the negative relationship for earnings

aggressiveness and earnings smoothing but finds a positive relationship for AEM and no

relationship with TEM as measured with the Jones model. Lastly, we find that implementing

ERM increases AEM in firms with high earnings volatility. In such firms, managers might

have a particularly strong incentive to engage in earnings smoothing. However, as ERM prevents the use of TEM for achieving a smoother earnings stream, it introduces an incentive

to engage in more AEM to achieve this goal.

We make several contributions to the emerging literature on the benefits and costs of ERM.

First, we support previous evidence on the determinants for implementing ERM. Second, we

8 document how ERM might contribute to shareholder value by mitigating opportunistic earnings management. Third, we refine the predictions about the performance effects of ERM by documenting the simultaneous effects on earnings volatility and earnings levels. Previous research has addressed only one of these two effects at a time. This improved specification may provide on explanation why prior research has found only muted effects of ERM on performance. To our knowledge, none of these observations has previously been documented as integrative as in our study.

3. HYPOTHESIS DEVELOPMET

ERM is an institutionalized governance and control system that complements traditional governance and control structures and processes. First, by creating comprehensive risk accountabilities and reporting mechanisms, it increases both the information about and the scrutiny of the business by the corresponding institutions on board level, such as risk and audit committees (Lam, 2003), and on management level, such as the internal audit function.

It hence improves detecting, monitoring and mitigating earnings management as a factor of risk. 3 For example, while audit committees are traditionally monitoring quality and regulatory compliance of financial reporting, risk committees are tasked with expanding the scope of monitoring to non-compliance risks of strategy and operations. Second, an ERM management committee might be implemented to signal commitment to regulatory compliance or to impose an element of self-monitoring that might be more effective in curbing earnings management than outside monitoring. Outside monitoring is inherently constrained by

3 AEM entails the risks associated with its reversal and TEM entails the risks associated with entering sub- optimal actions, foregoing value-enhancing transactions, or mistiming transactions. Both forms of earnings management might result in loss of reputation, legal penalties, and negative stock price reactions when detected or corrected. Accounting restatements to reverse AEM results in both short- and long term adverse stock market consequences. For example, announcing accounting misstatements results in negative one-day stock market returns of -9-10% (Feroz et al. 1991 and Dechow, Hutton and Sloan 1996) and improved stock market performance in the three-year post- detection period happens only for firms that improve their (Farber 2005).

9 unobservable managerial intentions and information asymmetries about the optimality of business transactions or reasonableness of accruals.

Thus, earnings management as a risk-generating accounting practice and earnings volatility as an accounting manifestation of risk might be considered as variables that ERM intends to address and that might hence also be among the determinants of the decision to implement

ERM. We deal with this potential endogeneity by analyzing the dynamic interaction between

EM and ERM. In a first stage, we analyze whether EM practices and earnings volatility are among the set of determinants for establishing an ERM committee. We use the results of this analysis in a second stage in which we use matching estimators to test the impact of implementing an ERM committee on subsequent EM practices, earnings distribution, and firm performance.

We are ex ante neutral on the possible relationship between earnings management or earnings volatility and the decision to establish an ERM committee. The reason is that either relationship could be supported by established theories. On the one hand, the quality and transparency on financial information is becoming a potential risk for companies, especially since financial scandals such Enron and WorldCom. Neoclassical theories can explain the incentives for companies to signal commitment to regulatory compliance and financial reporting quality. Accordingly, firms with already low earnings management or low earnings volatility would be motivated to establish ERM committees to reinforce their commitment signal.On the other hand, stakeholder theory would suggest the opposite. Firms with high levels of earnings management or high earnings volatility might want to mitigate risky practices and reduce volatility by implementing an additional and more effective mechanism of risk control.

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H1a (stakeholder): High EM (both AEM and TEM) will increase the probability of implementing an ERM committee.

H1b (signaling): Low EM (both AEM and TEM) will increase the probability of implementing an ERM committee.

H2a (stakeholder): High earnings volatility (EVOL) will increase the probability of implementing an ERM committee;

H2b (signaling): Low earnings volatility (EVOL) will increase the probability of implementing an ERM committee.

In a second stage we look at the effects of implementing the ERM committee on subsequent

EM and earnings distribution. Financial theory suggests that firms have traditionally been concerned in reducing the costs associated with conflicts of interest between owners and managers and between shareholders and bondholders, expected bankruptcy costs, and also the costs of regulatory scrutiny. Pressure from a range of sources (NYSE, 2004; SOX 2002) has been interpreted as an increased emphasis on transparency and completeness of disclosures of trend and other qualitative information. Outside monitoring might be less effective in detecting analyzing the optimality of business transactions or reasonableness of accruals. The incentives and the actual level of earnings management are hence difficult to detect by outsiders. This is particularly the case for TEM that is arguably less subject to monitoring by directors, auditors, regulators and other outside stakeholders (Kim & Sohn,

2013). Therefore, by making the possible real economic consequences of earnings management transparent to directors and managers, ERM creates awareness of its legal, economic or reputational risks and hence might attack the propensity for earnings

11 management at its root. In sum, we argue that ERM limits managerial opportunities to engage in earnings management and hence propose the following hypothesis:

H3 : Firms that establish an ERM committee in a given period will engage in H3a : less AEM H3b : less TEM in subsequent periods than do firms without such a committee.

Much of the ERM literature argues that the earnings benefit of ERM is a lower earnings

volatility due to reduced cross-sectional risks (Liebenberg & Hoyt, 2003), but has not yet provided any clear evidence of this effect. However, lower earnings volatility is not per se the objective of ERM. The ultimate benefit of ERM is that it supports managers in optimizing the firm’s overall risk-performance relation and enhancing firm value by means of better overall management, more coordinated management and loss avoidance (Pagach & Warr, 2010) as well as by preventing risks from aggregating across different sources (Liebenberg & Hoyt,

2003). Such optimization can happen along two dimensions: managers can either reduce risks for a given (target) level of performance or improve performance for a given (target) level of risk, respectively. Reducing risks translates into a lower volatility of earnings while improving performance leads to a higher average level of earnings, both effects crowding out the incentive for managers to engage in earnings management directed at the same purposes.

To illustrate, consider what we might call raw earnings (the hypothetical construct of earnings before both ERM and EM), risk-managed earnings (the hypothetical construct of earnings after ERM but before EM), and earnings-managed earnings (the observed construct of reported earnings after both ERM and EM). Risk-managed earnings display a lower volatility (higher level) of earnings than raw earnings. Put differently, we argue that conditional earnings volatility – holding the earnings level constant – and conditional

12 earnings level – holding the earnings volatility constant – might be better measures of the effects of ERM than unconditional earnings volatility. Therefore, if a manager desires to opportunistically manipulate earnings to reduce their volatility (increase their level), she needs to engage in less EM if risk-managed earnings are the point-of-departure as compared to the raw earnings. Accordingly:

H4 : Firms that establish an ERM committee in a given period will display either H4a : a lower earnings volatility or H4b : a lower earnings mean in subsequent periods than do firms without such a committee.

However, we also expect that ERM, just like any economic activity, is inevitably subject to a

cost-benefit tradeoff. The costs associated with the described benefits possibly introduce

some incentives for more earnings management. We aptly frame this cost-benefit tradeoff in

terms of the risk-return relation. Reducing earnings volatility (less risk) is expected to be

accompanied with an (weakly) adverse effect on average earnings (less return) and increasing

earnings level (more return) is expected to be accompanied with an (weakly) adverse effect

on volatility (more risk), respectively. Because lower levels of earnings are typically

undesirable for managers, these costs introduce an incentive to inflate earnings, counteracting

the decreased incentive and opportunities described above. In the second case, the managerial

incentive to inflate earnings is less prevalent but the incentive for smoothing persists.

We expect that these undesirable incentives are more prevalent in firms that display extreme

magnitudes of volatility and earning levels, i.e. high volatility and low earnings means.

Therefore, our last hypotheses follow on the previous argument that firms might follow a

strategy of either reducing earnings volatility or increasing their earnings level to optimize

13 their risk-performance relation as the consequence of a more explicit risk management. For example, a firm with high earnings volatility might engage in a better business management with better timed transactions.

H5 : Firms that display high earnings volatility and that establish an ERM committee in a given period will engage in H5a : more AEM H5b : more TEM in subsequent periods than do firms without such a committee.

The opposite argument applies to firms that are in a low-earnings spectrum. Implementing

ERM and incurring the associated costs would further depress their earnings. This is undesirable and hence introduces an incentive to engage in more earnings management to counteract this effect. We hence hypothesize:

H6 : Firms that display low earnings mean and that establish an ERM committee in a given period will engage in H6a : more AEM H6b : more TEM in subsequent periods than do firms without such a committee.

4. RESEARCH DESIG AD DATA

4.1. Sample

The ERM committee sample used for this study was derived from a Lexis-Nexis word search for companies that indicated they in their SEC filings they have an ERC committee or similar. Following Hoyt and Liebenberg (2009), firms were initially identified as having an

ERM committee based on a search of the following key terms: Strategic risk Management

14 Committee, Enterprise Risk Management Committee, Compliance and Operational Risk

Committee, Enterprise Risk Teams, Internal Risk Control Group, Corporate Financial Risk

Management Committee, Corporate Risk Committee, Enterprise Risk Council, Risk

Management Committee, Risk Committee of Management, Operational Risk Management

Committee, Operational Risk Management Committee, or Risk Working Group. The sentences that contain the key words were read to get a better sense of whether or not the

ERM concept is actually being used. Appendix A provides three examples of disclosures concerning the implementation of ERM committee in firms. Based on the keywords searching process, 531 US firms were identified as having created an ERM committee between 1993 and 2010, a total of 8,113 firm-year observations. We exclude 3,569 firm-year observations of financial institutions (SIC codes 6000-6999) because their accounting regulation differ too much from the rest of companies. Lexis-Nexis database searches across the complete population sample of Compustat companies, therefore the remaining companies in Compustat database, do not implement an ERMC. Total firm-year observations of the sample, with the main descriptive statistics, are summarized in Table 2.

4.2. Measures for Enterprise Risk Management and Earnings Management

4.2.1. Enterprise Risk Management Measure

A major obstacle to empirical ERM-related research is the difficulty in identifying firms that

are indeed engaging in ERM. Researchers find difficult to detect if the firm is managing risks

in a disaggregated or aggregated manner. Thus they are forced to either rely on survey data or

search for a signal of the existence of ERM programs. One such signal may come from the

creation of a specialized managerial position, the Chief Risk Officer (CRO), which is

responsible for ERM implementation and coordination (Liebenberg & Hoyt, 2003). However

we understand that the existence of an Enterprise Risk Management Committee (ERMC), or

15 similar, is a better proxy for the implementation of a holistic risk management structure.

ERM is a dummy variable which takes value of 1 if the company has a risk committee at the managerial level and 0 otherwise.

4.2.2. Accrual-based Earnings Management measures

Prior studies on earnings management use different discretionary models to proxy for AEM.

Measures for AEM lack of power and are highly noisy by construction. We construct an

aggregate measure capturing earnings smoothing (Leuz et al, 2003), earnings aggressiveness

(Leuz et al, 2003) and discretionary accruals (Dechow, Sloan, & Sweeney, 1995).

• EM1: Earnings Smoothness (Leuz et al, 2003)

(σ(OPINC it/TA it-1)/ σ(CFO 4it/TA it-1) )

• EM3: Earnings Aggressiveness (Leuz et al, 2003)

[|ACCit |/ TA i,t-1] / [|CFOit| /TA i,t-1]5

• Jones: Modified Jones Model (Dechow et al, 1995)

ACC it / TAit-1= β0 + β1(1/TA it-1) + β2(∆SALE it /TA it-1–∆REC it /TA it-1) + β3PPE it /TA it-1+ εit ,

WhereOPINC is DATAXX from Compustat, CFO is DATAXX from Compustat, ACC is

defined as the change in non- current assets minus the change in current liabilities

excluding the current portion of long-term debt, minus and , scaled by lagged total assets 6, CFO is DATAXX from Compustat, For Modified Jones Model ACCit

is computed as above, TAit-1 is lagged total assets, ∆SALE it is the change in sales, ∆REC it is the change in and PPE it is net property, plant, and equipment. We take

4The firm-level “rolling” standard deviations of operating income and operating both scaled by lagged total assets. 5Higher score simply more earnings mangement. 6 ACC = (∆total currentassets/TA – ∆cash/TA) - (∆total currentliabilities/TA - ∆short-termdebt/TA - ∆taxes payable/TA) - depreciationexpense/TA

16 the absolute value of the residual from the above regression as a measure of discretionary accruals of firm iat time t.

AEM1: Aggregate measure of Accrual Earnings Management

AEM1 is the mean of rank_em1re1, rank_em3 and rank_jones, where rank_em1re is the deviation of em1 from one, rank_em3 is the rank of em3 as in equation (x+2) and rank_jones is the rank of the residual absolute values of Jones as in equation (x+3). Rank_em1re1 is a proxy for earnings management considering both smoothness and aggressiveness.

AEM2 is the rank Jones measure

4.2.3. Transaction-based Earnings Management Measures

To proxy for Transaction earnings management (TEM) we follow the model developed by

Roychowdhury (2006), and implemented by Cohen, Dey and Lys (2008) that consider the abnormal levels of cash flow from operations (CFO), discretionary and productions costs to proxy real activities manipulations. We focus on three manipulation methods and their impact on the above three variables: acceleration of the timing of sales through increased price discounts or more lenient credit terms; reporting of lower through increased production, and decreases in discretionary expenses which include advertising , research and development, and SG&A expenses.

The normal levels of CFO, discretionary expenses and production costs are generated using the model developed by Dechow, Kothari and Watts (1998) as implemented in

Roychowdhury (2006). We express normal CFO as a linear function of sales and change in sales. To estimate this model, we run the following cross-sectional regression for each industry and year:

17 CFO it /TA it-1 = k1t (1/ASS it-1) + k 2(SALE it /ASS it-1) + k 3 (∆SALE it /TA it-1) + ε it

Abnormal CFO is actual CFO minus the normal level of CFO calculated using the estimated coefficient from (4).

Following Cohen, Dey and Lys (2008) production costs are defined as the sum of cost of goods sold (COGS) and change in during the year.

COGS it /TA it-1= k 1t (1/TA it-1) + k 2SALE it /TA it-1 + ε it

∆INV it /TA it-1 = k 1t + (1/TA it-1) + k 2∆SALE it /TA it-1+ k 3∆SALE it-1/TA it-1 + ε it

Adding the previous equations the normal level of production costs is estimated as follows:

PROD it /TA it-1 = k 1t (1/TA it-1) + k 2SALE it /TA it-1+ k 3∆SALE it /TA it-1+ k 4∆SALE it-1/TA it-1 + ε it

The normal level of discretionary expenses is a linear function of current sales; however

Cohen et al (2008) address the fact that some companies might manage upwards reported earnings certain year, resulting in lower residuals by modeling discretionary expenses as a function of lagged sales 7:

DISCREX it /TA it = k 1t (1/TA it-1) + k 2SALE it-1 /TA it-1 + ε it

The abnormal levels of CFO, production costs and discretionary expenses are computed as

the difference between the values reported in COMPUSTAT and the levels predicted in previous equations. We rank these three variables in its absolute values and average them to

generate an aggregated measure as proxy for transaction earnings management.

7 CFO is cash flow from operations in period t (Compustat data item 308 – annual Compustat data item 124); Prod represents the production costs in period t, defined as the sum of COGS (annual Compustat data item 41) and the change in (annual Compustat data item 3); DiscExp represents the discretionary expenditures in period t, defined as the sum of advertising expenses (annual Compustat data item 45), R&D expenses (annual Compustat data item 46)16 and SG&A (annual Compustat data item 189).

18 4.2.4. Control Variables

Beasley, Pagach and Warr, 2007 find that firms with less cash and more leverage are likely to see benefits from ERM. Furthermore, shareholders of large non-financial firms, with volatile earnings, low amounts of leverage and low amounts of cash on hand also react favorably to the implementation of ERM. These findings are consistent with the idea that a well implemented ERM program can create value when it reduces the likelihood of costly lower tail outcomes, such as financial distress.

Financial literature also relates ERM with firm value and use Tobin’s Q as a proxy for firm value since TQ compares the market value of a firm’s assets to their replacement cost. It has been used to measure (Yermack, 1996; Morck, Schleifer, and Vishny, 1988; (Hoyt &

Liebenberg, 2011). We will control for systematic risk using Beta KMV (BETA) (Acharya,

Almeida and Campello, (2012). This unlevered beta measures the amount of systematic risk

inherent in a firm's compared with the overall market.

We control for size to control for political costs related to size (SIZE), since it is often used to proxy for political costs (e.g. Watts & Zimmerman 1986). However, firm size could proxy

for factors other than political visibility such as information environment, capital market pressure, or financial resources. Several studies hypothesize fixed costs associated with

maintaining adequate internal control procedures, and hence predict a positive relation between firm size and internal control quality (Dechow, Ge, & Schrand, 2010). We control

for firm performance (ROA) since studies hypothesize that weak financial performance provides incentives for earnings management using the discretion allowed in accounting standard adoption to their advantage (Petroni, 1992; DeFond and Park, 1997; Balsam, Haw,

19 and Lilien, (1995 ); Keating and Zimmerman , 1999; Doyle, Ge, and McVay, 2007; Kinney and McDaniel, 1989). We control for growth (TQ) on the fundamental element of earnings properties; growth also is associated with greater measurement error and more manipulation opportunities (Richardson et al., 2005). We control for leverage (LEV) since several studies find evidence on positive relationship between highly levered firms and EM (Kinney and

McDaniel, 1989; Efendi et al., 2007; Dechow et al., 1996). Finally we control for GDP growth (GDP) to capture the effect of “good” times and “bad” times on EM. Table 1 provides detailed information on the measures for these control variables.

5. METHODOLOGY AD EMPIRICAL MODELS

5.1. Time-to-implementation models

Our first hypotheses postulate that the decision of implementing ERM is a function of

EVOL (H0a), EMEAN (H0b) and EM (H0c). In order to test the hypotheses, we set up a

“time-to-implement” discrete-time duration model, where we specify the hazard rate (the

probability of implementing ERM at time t conditional on not having implemented before

time t) is a logit function of time-varying variables. We estimate the following logit

model:

= 1| = 0, < =

β β β β β Σβ , , , , ,, = β β β β β Σβ , , , , ,,

(1)

where ERMC i,t , identifies all periods t where company i discloses in 10K the existence of an

ERM committee; EMEAN i,t-1, , EVOL i,t-1, AEM i,t-1 and TEM i,t-1 are the measure of earnings volatility, earnings mean, accrual-based and transaction-based EM for firm i at time t-1. Note

20 the conditioning in the probability: in order for our specification to be a correct "time to implement" model, we need to setup the data for implementing so that for each ERM company i the data for the dependent variable ERMC it are zeros for the periods before disclosure, a one for the disclosure year and the periods after disclosure are eliminated from the dataset (see Jenkins, 1995). For non-ERM companies, the full time series (of zeros) is included, representing a censored observation (i.e., an observation for which the event, disclosure, is not observed in the sample period). Once the data have been conveniently structured, we can estimate the above equation as a traditional logit model.

5.2. Regression models for impact of implementing ERMC

Given that initiation of ERM may have different implications from continued ERM and some of our hypotheses relate to effects over time after disclosure, we test for the impact of ERM with traditional fixed-effects panel regressions. In order to test H2, impact of ERM on EVOL and EMEAN, we estimate the following panel:

EVOL = β + βTime + βERMC , + ΣβCONTROLS ,, + α + ε, 2)

EMEAN = β + βTime + βERMC , + ΣβCONTROLS ,, + α + ε, (3)

where Time is a time trend, EVOL it is a measure of earnings volatility and EMEAN it is a measure of earnings mean of company i at period t.

Hypothesis 3a and 3b, ERM companies present less EM over time, is tested with fixed-effects panels of the form:

AEM = β + βERMC , + ΣβCONTROLS ,, + α + ε, (4)

TEM = β + βERMC , + ΣβCONTROLS ,, + α + ε, (5)

21 where AEM it is a measure of accrual-based EM and TEM it is a measure for transaction-based

EM for company i at period t.

In H4a-H4b, we test the effect of ERM implementation in companies with high values of earnings volatility (H_VOL) and in companies with low earnings mean (L_MEAN).

AEM = β + βERMC , + βH_VOL , + βERMC , ∗ H_VOL , + βL_MEAN , +

βERMC , ∗ L_MEAN , + ΣβCONTROLS ,, + α + ε, (6)

TEM = β + βERMC , + βH_VOL , + βERMC , ∗ H_VOL , + βL_MEAN , +

βERMC , ∗ L_MEAN , + ΣβCONTROLS ,, + α + ε, (7)

where H_VOLit identifies company i at period t observations on the 4 th quartile of earnings volatility, and L_MEANit identifies company i at period t on the 1 st quartile of earnings mean.

5.3. Matching estimators

In hypotheses H1 and H2 we analyze the impact of ERM implementation on AEM (H1a),

TEM (H1b), EVOL (H2a) and EMEAN (H2b). Although, the "time to implement" model (1) suggests that the decision to implement ERM is not directly related to EM, it appears to be negatively related to EVOL and EMEAN, thus the decision is endogenous. In order to alleviate the effects of this endogeneity and measure the impact of implementing ERM on earnings management (H2), and earnings volatility and mean (H3), we use matching estimators (Roberts and Whited, 2012).

In particular, for each company which implements an ERM committee during our sample we

22 find a matching company among the "non-ERM" group of companies, i.e., among those that do not implement a committee during the sample. We do this matching in two ways:

a) Propensity-score matching: we take the value of the estimated probability of implementing

ERM coming from model (1) for every company which implements ERM committee. We then look for the non-disclosing company with the closest value of the estimated probability in the same year of ERMC implementation. b) Matching on observables: for every company which implements ERMC, we find in the year of implementation the non-ERM company which is the most similar in its values of the matching variables. 8 For the "closest" match we use a Mahalanobis distance on the matching

variables (Roberts and Whited, 2012).

Once we have the set of non-disclosing companies which are "close matches" to those which

initiate disclosure, we compare the changes over time in EM measures and EVOL, EMEAN

and ROA measures for the two sets of companies, and run significance tests on the

differences for several time horizons (1 to 5 years) between the two groups. This procedure

reduces the impact of the endogeneity of the decision to initiate disclosure, at least to the

extent that our time-to-disclosure model or the matching observables are indeed related to the

decision to initiate disclosure.

6. RESULTS

6.1. Descriptive analysis

Table 1 describes all the variables and sources that we have used in our main analyses, including the "intermediate" rank variables used to construct the EM measures (Panel A) and

8 The specific model used for the propensity score and the set of matching variables are specified in the output tables (Table 3).

23 table 2 reports some basic descriptive statistics divided in two columns. First column shows

ERM firms : all firms that at some point have implemented an ERM committee, information

available on SEC files (automatic search through Lexis-Nexis). Second column shows on-

ERM firms : firms which do not mention any ERM committee on SEC files database at any point in time. Note that the two groups differ along several dimensions: ERM companies are

larger in size, more indebted and more global (GLB). They also show higher performance

(ROA) and lower unlevered risk (BETA).

Mean values for earnings smoothing (Rank_em1) and earnings aggressiveness (Rank_em3)

are lower for ERM-firms than for non-ERMC firms, while the rest of EM measures are

higher for ERM-firms. Earnings volatility (EVOL) is lower for ERM-firms (.07 vs .22), on

the other hand earnings mean (EMEAN) are higher and (.07 vs -.08) and with significantly

smaller standard deviations companies implementing ERM committees. All in all, we observe

significant differences between the groups of firms based on the existence of ERM

committees which justify our analysis, especially the matching estimators described in

section 5.3.

6.2. The role of earnings volatility, earnings mean and EM in implementing ERM

committee

We report the results using earnings smoothing (rank_em1) the mean rank of three accrual based measures AEM1 (rank_em1re1, rank_em3 and rank_jones) and the rank of jones- based measure AEM2. We also report results for transaction based earnings management using TEM1 (mean rank of AB_CFO, AB_PROD and AB_DISCREX) and TEM2 (rank mean of AB_CFO and AB_PROD) to increase number of observations since AB_DISCREX reduces observations considerably.

Hypotheses H1a-H1b and H2a-H2b postulated that EM (TEM and AEM) and EVOL could be

24 either positively or negatively related to the probability of implementing ERM committee.

Tables 3a-3b show the results of the discrete-time duration model (1) for the probability of implementing a ERM committee (ERMC). In Table 3a, we show several versions of the baseline model which differ on the EM proxy and time effects. Past levels of EVOL are negatively related (p<0.1) in baseline model and after controlling for time effects. Parameters for past values of AEM1 are negative but no significant in any specification model. Past values of AEM2 are positively related (p<0.1) to probability of implementing ERMC and significance increases to 5% when controlling for time effects. Conversely, past values of

TEM1 are negatively related and significant (p<0.05) in baseline model, however significance is lost when controlling for time effects. TEM2 is not significant in any case. In table 3b we include both AEM and TEM and significance is mostly lost. TEM2 is the only measure of earnings management that remains significant (p<0.05) and negatively related to the probability of implementing a ERMC (in both specifications with and without time effects). Note that in the TEM1 specifications the number of observations is much lower and significance is lost. Control variables such as debt (LEV), size (SIZE) and firm performance

(ROA) are significant and positively related to the probability of implementing ERM committee; liquidity (LIQ) and unlevered risk (BETA) are negatively related at significance levels of at least 1% of significance.

These results support a negative link between past transaction-based EM and past levels of

earnings volatility with the implementation of ERM committees. ERM control system may

have been used by the managers as a way to signal for good performance in what concerns to

certain accounting practices, especially those related to business transactions. This strategic

use of corporate governance mechanisms is in line with previous findings of a positive link between ERM and firm performance (ROA) and lower earnings volatility (EVOL).

25 On the other hand, our results show a non strong positive relationship between past values of

AEM2 and implementation of an ERM committee; conversely we find a negative and stronger relationship between past values of EMEAN and the decision of implementation of

ERM committee. These results might suggest a strategy to compensate misleading accounting practices and poor past performance by implementing a more effective and integrated control system. In the next analyses we show how ERM implementation is negatively related to subsequent EM and EVOL as well as positively related to firm performance (ROA).

6.3. The effect of implementation of ERM on EM

H3a-H3b postulated that implementation of ERM would lead to subsequent lower levels of

EM (both AEM and TEM). Tables 4a-4b show significance tests on the differences between the levels of the main variables for the two groups of before and after implementation of

ERM committee. Table 4a shows the mean measures for propensity score-based match before and after the implementation of the committee. In the pre implementation columns differences between the two subsamples are not significant except for rank_em1, rank_em1re1, AEM2 and TQ, which are larger for No ERM firms. After implementation of the ERM committee these variables (and rank_em3) remain significantly larger for No ERM firms. Table 4b shows the results for the matching on observables model. In this case ERM firms and No ERM firms are similar in all variables except rank_em1re and BIG4, where No-

ERM means are larger. After implementation we find significant differences in rank_em1, rank_em1re1, rank_em3, AEM1, AEM2, ROA and SIZE.

Table 5 shows the results of the matching analysis for the impact of initiation of disclosure on subsequent levels of EM. Thus, we decide to focus on the changes in EM proxies with respect to the initial levels for five different time horizons using diff-in-diff estimators. We find significant and negative differences for rank_em1, rank_em1re1, rank_em3. Differences for

26 AEM1 are negative but not significant and differences for AEM2 are positive and significant for the first year after implementation in the propensity-score model. TEM1 is negatively significant in matching on observables for year one and four, and positive in propensity score matching for the fourth and fifth year. This is probably due to the low number of matches available, and to the admittedly crude measures of EM. TEM2 is positively significant for third year on propensity score matching. In other words, companies which implement an

ERM committee tend to show lower levels of aggressiveness and smoothness in their earnings than companies which do not, especially for first time horizons.

In Table 6 we show the results of model (?) which analyzes the effects of continued effect of

ERM committee on subsequent EM. We find a significant negative relation between lagged

ERMC and EM. Hence, continued effect of ERM committee seems to lower the extent to which companies engage in opportunistic behaviors. In the following sections we will analyze the impact of ERM on earnings volatility (EVOL), earnings mean (EMEAN) and

ROA (for completeness).

6.4. The effect of implementing ERM on Earnings Volatility, Earnings Mean and Firm

Performance

In H4a-H4b we postulate, as suggested in risk management literature, that implementation of

ERM will reduce earnings volatility (EVOL) and increase earnings mean (EMEAN). Table 7 shows the results of the diff-in-diffs matching estimators for changes in EVOL, EMEAN and

ROA after implementation of ERMC. The results for EVOL are positive and significant especially for time horizons beyond two years in both matching models. ROA is positive in both models and significant for the later periods. EMEAN differences are not significant.

These results support previous literature suggesting a positive impact of implementation of

ERM systems on firm performance and volatility of earnings.

27 In Table 8 we show the results for the continuous effect of the ERMC on EVOL and

EMEAN. We find significant negative relation between ERMC and EVOL, conversely we do not find significant effect of the implementation of the ERM committee on the EMEAN. In order to elaborate on this result, we analyze the impact of ERMC on EM in companies with high and low levels of earnings volatility and mean.

Variable H_Vol and L_Vol are dummy variables taking value of 1 if the company has is on the 4 th and 1 st quartile of EVOL respectively. ERMC_HV and ERMC_LV are the interaction

of H_Vol and L_Vol with ERMC, respectively. H_Mean and L_Mean are dummy variables

taking values of 1 if the company is in the 4 th and 1 st quartile of EMEAN respectively.

Accordingly, ERMC_HM and ERMC_LM are the interactions of H_Mean and L_Mean with

ERMC respectively. Note in Table 9 that coefficients for ERMC are negative and significant

for all EM proxies. Parameters for ERMC_LV, interaction of ERMC and L_Vol, are negative

for all EM measures but significant for TEM1 and TEM2 (p< 0.01; p<0.05). On the other

hand, coefficients for ERMC_HV, in Table 10 are positive for all EM measures and

significant (p< 0.01) for EM1 and TEM2. These results suggest that the impact of the ERM

committee turns positive in companies with relatively high levels of earnings volatility.

7. COCLUSIOS

In this paper we have taken a first step in examining the role of enterprise risk management

(ERM) in accounting practices that relate to risk. Traditionally, audit committees at the Board

level have held oversight over accounting risks. Recently, though, companies have started to

integrate risk management functions within the firms’ operational mechanisms, providing

them with resources and incentives to monitor and reign in opportunistic behaviors.

Companies implementing such managerial based risk control system might also have interest

in reducing EM practices, or reduce volatility (risk), making the decision endogenous on

28 those variables. To alleviate the impact of endogeneity in our results we have accounted for the dynamic relationship between ERM and EM by considering how past EM affects implementation of ERM and how ERM committees relate to subsequent EM. Our results suggest a negative relationship between past values of earnings volatility and transaction based earnings management and the probability of implementing an ERM committee. That is aligned with theories suggesting that companies signal their good practices by implementing a new and more integrated risk monitoring strategy. Conversely, we find a positive relation between accrual-based measures and the probability of implementing an ERM committee.

This can be explained by the use of these companies of AEM to reduce the volatility of the earnings. On a second stage, our results show the negative impact of implementing ERM on accrual-based earnings management, but a not so clear impact on transaction-based practices.

This paper has also contributes with some evidence on the effect of ERM on earnings volatility, level of earnings and firm performance. Our findings suggest that the implementation of ERM committees decrease earnings volatility and increase firm performance, while has no impact on the level of earrings mean. These results are aligned with previous literature suggesting negative relationship between ERM and earnings volatility and performance. We also analyze the special case of companies with extremely high and low earnings volatility and mean. Our findings suggest that the implementation of

ERM in on low volatility companies increases the negative impact on EM, while implementation of the committee reduces the negative impact on companies with highly volatile earnings. This can be attributable to the fact that those companies were not largely engaging in earnings management to smooth these risk indicators and therefore the trade-off is found to be adequate and within risk threshold acceptance.

29 Overall, our results are consistent with the transparent financial reporting hypothesis, which is in line with the notion, that risk management activities are motivated by managers’ incentives to lower the reputational, legal and economic risks of the company. To the extent that our results hold after controlling for firm size and financial performance, two other ERM incentives that might explain the negative relation between ERM and EM, the evidence in our study lends support to the argument that ERM firms are more prudent in financial reporting to serve the interests of all stakeholders. We consider our findings with regard to financial transparency a first step in a stream that examines issues such as risk as a factor affecting corporate financial reporting. These findings contribute to the literature about how governance and control mechanisms interrelate with earnings management. This can help to comprehend the effectiveness of risk management in restringing accounting manipulation.

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32 Table 1. Description of variables

Variable Source Description ame Panel A: rank variables used to construct EM measures rank_em1re1 it Rank in year t of firm i's value of |σ(op_inc)/σ(cfo) -1|, where Compustat - own σ(z) is the standard deviation of z computed using past data (Leuz et al., 2003) rank_em3 it Rank in year t of firm i's value of (|∆acc|/|∆cfo| (Leuz et al., Compustat - own 2003) rank_jones it Rank in year t of firm i's value of the residual from a Compustat - own modified Jone's Model (Dechow et al., 1995) rank_cfo it Rank in year t of firm i's abnormal operations cash flow Compustat - own (Roychowdhury, 2006) rank_prod it Rank in year t of firm i's abnormal production Compustat - own (Roychowdhury, 2006) rank_discrex Rank in year t of firm i's abnormal discretionary expenses Compustat - own it (Roychowdhury, 2006) Panel B: variables used in the analysis of H0 and H1 ERMC Dummy: 1 if company has a Enterprise Risk Management LEXIS-NEXIS Commi ttee in year t, 0 otherwise BETA it Unlevered Betas from Merton-KMV model Moody’s KMV LEV it Total debt it / Total Assets it Compustat AEM1 it Average for firm i of year t values of rank_em1re1 it , Compustat - own rank_em3 it and rank_jones it AEM2 it Average for firm i of year t values of rank_jones it Compustat - own TEM1 it Average for firm i of year t values of rank_cfo it , Compustat - own rank_prod it and rank_discrex it TEM2 it Average for firm i of year t values of Compustat - own rank_cfo it and rank_prod it

33

Table 1. Description of variables (cont.)

EVOL it Firm i’s average earnings volatility from period t-5 to t. Compustat - own

EMEA it Firm i’s average earnings mean from period t-5 to t. Compustat - own th H_VOL it Dummy: 1 if company’s EVOL is in 4 quartile Compustat - own

ERMC_HV it Interaction of H_VOL it and ERMC it Compustat - own th H_MEA it Dummy: 1 if company’s EVOL is in 4 quartile Compustat - own

ERMC_HM it Interaction of H_MEA it and ERMC it Compustat - own st L_VOL it Dummy: 1 if company’s EVOL is in 1 quartile Compustat - own ERMC_LV it Interaction of H_VOL it and ERMC it Compustat - own st L_MEA it Dummy: 1 if company’s EVOL is in 1 quartile Compustat - own ERMC_LM it Interaction of H_MEA it and ERMC it Compustat - own Panel D: control variables

ROA it Income before extraordinary items it / Total assets it Compustat LIT i Dummy: 1 if company iis in a high litigation industry, 0 See references otherwise (Francis et al. 1994, Skinner 1997, Matsumoto 2002) SIZE it Log (market value of equity) it Compustat GLB it Dummy: 1 if company ireports foreign income in year t, 0 Compustat otherwise TQ it Equity Market V it + Liabilities MarketV it / Equity BookV it + Compustat Liabilities BookV it LIQ it Nr shares of firm i’s traded during period t/ Total shares Compustat outstanding end of period t GDP t GDP growth rate in year t Federal

BIG4 it Dummy: 1 if firm i's auditor in year t is a "Big Four", 0 Compustat otherwise Sources: Compustat - own : own calculations from Compustat variables; KLD : database on CSR by....; LEXIS-EXIS : search for information on disclosure of CSR activities; Federal Reserve : FRED database from the Federal Reserve Bank of St. Louis; Compustat : variables directly available in Compustat. Interaction variables and additional variables used in some of the analyses are described in the captions of each specific table.

34 Table 2. Descriptive Statistics

ERM vs non-ERM firms ERM firms on-ERM firms Tests Variable Mean St. dev. Median Mean St. dev. Median p-value p-value (firm-year) (firm-year) (means) (medians) ERMC 4,544 .23 .42 0.00 313,024 ------Rank_em1 2,045 2,201.83 1,683.32 1,811.00 157,929 2,687.70 1,877.86 2,348.00 0.000 0.000 Rank_em1re1 2,046 2,274.92 1,693.44 1,883.00 158,031 2,687.49 1,878.99 2,347.00 0.000 0.000 Rank_em3 912 3,065.81 2,013.12 2,975.00 117,132 3,431.38 2,085.83 3,327.00 0.000 0.000 AEM1 503 3389.00 1159.09 3292.66 64,336 3,240.32 1,220.13 3,183.00 0.006 0.128 AEM2 3,067 4221.79 1758.69 4238 213,981 3,017.80 1,983.18 2,755.00 0.000 0.000 TEM1 250 2903.26 1148.13 3071 25,482 2,469.29 1,201.88 2,444.50 0.000 0.000 TEM2 2,252 4592.19 1661.83 4,713.5 132,655 3,550.22 1,829.52 3,544.50 0.000 0.000 BETA 2,056 .55 .72 .41 137,486 .96 1.23 .84 0.000 0.000 EVOL 2,056 .07 .14 .03 110,033 .22 .33 .10 0.000 0.000 EMEA 4,479 .07 .16 .09 110,033 -.08 .48 .07 0.000 0.000 ROA 4,544 .02 .18 .04 290,994 -.12 .57 .03 0.000 0.000 LIT 1,521 .06 .23 0.00 313,024 .19 .39 0.00 0.000 0.000 SIZE 716 7.35 1.99 7.60 83,573 4.61 2.46 4.62 0.000 0.000 GLB 4,544 .85 .35 1.00 45,215 .69 .46 1.00 0.000 -- BIG4 1,521 .45 .50 0.00 313,024 .43 .49 0.00 0.000 0.001 TQ 4,486 2.26 4.98 1.73 83,510 2.66 8.54 1.69 0.067 0.133 LEV 4,544 .35 .18 .37 290,412 .29 .33 .23 0.000 0.000 GDP 503 .03 .02 .03 313,024 .03 .02 .03 0.065 0.000 ROA, SIZE, TQ and LEV have been winsorized at the top and bottom one percent of their distributions. ERM firms : all firms that at some point have implemented an ERM committee, information available on SEC files (automatic search through Lexis-Nexis). on- ERM firms : firms which do not mention any ERMC on SEC files database at any point in time. The "Tests" columns show the p-value of a t-test for the difference in means of the different variables for the two groups (left column) and the p-value of a test for equality in the medians of the two groups (right column).

35 Table 3a. Determinants on Implementing ERMC - Basic models and time effects

Dependent variable: ERM Committee EVOL+ AEM1+ AEM2+ TEM1+ TEM2+ EVOL AEM1 AEM2 TEM1 TEM2 TIME TIME TIME TIME TIME -.03 .19*** .06* .28*** -.05 TIME (-0.75) (2.76) (1.70) (4.20) (-1.47) -.00 .00* -.00 .00** AEM.L1 (-1.03) (1.82) (-0.86) (2.10) -.00** .00 -.00 .00 TEM.L1 (-2.05) (0.87) (-1.17) (0.58) -2.42* -2.41* EVOL (-1.81) (-1.81) -1.97*** 1.58 -.85 -1.02 -.85* -1.98*** 2.02 -.84 -1.36** -.86* EMEAN (-3.20) (1.11) (-1.60) (-1.61) (-1.72) (-3.23) (1.17) (-1.53) (-2.26) (-1.79) 1.24* 1.13*** 1.32*** 1.38 1.49*** 1.21*** 1.18*** 1.33*** 1.70 1.47*** LEV (1.94) (2.82) (4.16) (1.00) (4.76) (4.04) (3.15) (4.29) (1.35) (4.57) -.40*** -.26 -.35** -.54** -.42*** -.41*** -.27* -.35** -.56*** -.41*** BETA (-2.73) (-1.61) (-2.26) (-2.51) (-2.76) (-2.69) (-1.77) (-2.41) (-2.96) (-2.63) 1.24* 1.45 1.36* 3.21* 1.27* 1.24** 1.25 1.32* 3.78* 1.27* ROA (1.94) (1.03) (1.91) (1.76) (1.72) (1.96) (0.83) (1.83) (1.85) (1.77) .39*** .19 .33*** .32*** .47*** .39*** .15 .32*** .31** .47*** SIZE (5.63) (1.59) (4.53) (2.69) (6.53) (5.57) (1.08) (4.16) (2.40) (6.52) -.46 .66 -.34 1.12** -.35 -.46 .71 -.32 1.28** -.37 LIT (-1.58) (1.10) (-1.19) (2.05) (-1.11) (-1.59) (1.18) (-1.12) (2.40) (-1.15) -.02 -.00 -.01 -.00 -.01 -.02 -.00 -.01 .00 -.01 TQ (-1.44) (-0.17) (-1.02) (-.0.03) (1.25) (-1.45) (-0.05) (-0.94) (0.32) (-1.31) -.00*** -.00 -.00*** -.00*** -.00*** .00*** -.00 -.00*** -.00*** -.00*** LIQ (-3.55) (-1.26) (-3.46) (-2.90) (4.40) (-3.24) (-1.43) (-3.87) (-3.80) (-3.81) -13.53*** 1.52 -11.12** -8.16 -10.08** -11.23 27.47 -4.77 15.95 -16.07** GDP (-2.95) (0.10) (-2.49) (-1.02) (-2.07( (-1.20) (1.24) (-.86) (1.59) (-2.37) N 41,073 14,335 38,867 9,311 37,960 41,073 14,335 38,867 9,311 37,960 Pseudo R 2 0.095 0.054 0.082 0.117 0.112 0.096 0.072 0.083 0.148 0.113 Maximum likelihood estimation of the discrete-time logit duration model (1) for the probability of implementing a ERM committee (ERMC); t- stats (in parentheses) are based on standard errors clustered by firm and year; *, ** and *** denote significance at the 10%, 5%, and 1% level based on a two-sided test. Column headings denote the specific basic and time . Estimates of the intercept have been omitted.

36

Table 3b. Determinants on Implementing ERMC – AEM-TEM combined models and time effects

Dependent variable: ERM Committee AEM1 AEM1 AEM2 AEM2 AEM1 AEM1 AEM2 AEM2

TEM1 TEM2 TEM1 TEM2 TEM1 TEM2 TEM1 TEM2 .37*** .13 .28*** .02 TIME (3.88) (1.55) (3.91) (0.65) -.00 -.00 .00 .00 -.00 -.00 -.00 .00 AEM.L1 (-1.09) (-0.41) (1.47) (1.47) (-0.62) (-0.30) (-0.41) (1.55) -.00 -.00** -.00 -.00 .00 -.00** -.00 -.00 TEM.L1 (-0.48) (-2.52) (-0.44) (-0.44) (0.24) (-2.12) (-0.87) (-0.38) 3.00 2.71** -.90 -.90 4.15 3.20* -1.34** -.90 EMEAN (1.31) (1.98) (-1.51) (-1.51) (1.39) (1.89) (-2.33) (-1.50) -.19 1.41*** 1.42*** 1.42*** .07 1.50*** 1.71 1.42*** LEV (-0.11) (3.47) (4.20) (4.20) (0.05) (3.87) (1.39) (4.27) -.29 -.22 -.35** -.35** -.26 -.22 -.56*** -.35** BETA (-0.69) (-1.35) (-2.00) (-2.00) (-0.52) (-1.41) (3.02) (-2.05) .73 1.29 1.47* 1.47* -.05 1.15 3.76* 1.46* ROA (0.36) (0.90) (1.79) (1.79) (-0.03) (0.74) (1.86) (1.76) .36 .29** .41*** .41*** .28 .26* .32** .40*** SIZE (1.63) (2.25) (6.15) (6.15) (1.13) (1.81) (2.44) (5.85) .24 .79 -.24 -.24 .39 .83 1.26** -.23 LIT (0.24) (1.50) (-0.84) (-0.84) (0.40) (1.57) (2.41) (-0.80) -.05** -.00 -.01 -.01 -.04 -.00 .00 -.01 TQ (-2.16) (-0.11) (-0.81) (-0.81) (-1.62) (-0.01) (0.25) (-0.78) -.00 -.00 -.00*** -.00*** -.00 -.00* -.00*** -.00*** LIQ (-0.86) (-1.62) (-4.63) (-4.63) (-1.00) (-1.70) (-3.96) (-4.74) -.38 1.64 -13.83*** -13.83*** 33.93 19.59 15.82 -11.52* GDP (-0.02) (0.10) (-3.00) (-3.00) (1.08) (0.86) (1.61) (-1.81) N 3,526 13,403 9,311 36,054 3,526 13,403 9,013 36,054 Pseudo R 2 0.085 0.082 0.117 0.097 0.138 0.090 0.149 0.097 Maximum likelihood estimation of the discrete-time logit duration model (1) for the probability of implementing a ERM committee (ERMC); t- stats (in parentheses) are based on standard errors clustered by firm and year; *, ** and *** denote significance at the 10%, 5%, and 1% level based on a two-sided test. Column headings denote the specific basic and time . Estimates of the intercept have been omitted.

37 Table 4a. Descriptive statistics before implementation of ERMC

PAEL A : p-score model (1)

Pre Implementation of ERMC Post Implementation of ERMC

ERM-firms oERM firms Test ERM-firms oERM-firms Test

Variable (firm- Mean (firm- Mean p-value (firm- Mean (firm- Mean p-value year) year) year) year) ERMC 74 1.00 61 -- -- 74 1.00 61 -- -- rank_em1 66 2,024.1 52 2,785.5 0.005*** 63 1,895.6 48 2,746.2 0.001*** rank_em1re1 66 2,080.7 52 2,630.7 0.040** 66 1,910.0 48 2,875.6 0.000*** rank_em3 24 3,357.9 36 3829.5 0.237 43 1,813.8 50 2,731.2 0.040** AEM1 21 3,696.9 27 3,835.5 0.646 21 3,512.8 28 3,700.9 0.394 AEM2 68 5,490.1 54 4,917.6 0.036** 68 4,966.5 54 4,510.9 0.089* TEM1 8 2,769.2 12 3,209.3 0.462 9 2,689.5 13 2,961.1 0.624 TEM2 71 5,237.9 53 5,269.1 0.894 69 4,825.8 50 4,769.8 0.828 EVOL 74 .10 61 .12 0.695 74 .09 61 .11 0.648 EMEA 74 .03 61 .09 0.230 74 .04 61 .09 0.212 ROA 74 -.007 61 -.003 0.904 74 -.000 61 -.020 0.567 LIT 74 .12 61 .06 0.275 74 .12 61 .06 0.270 SIZE 74 7.35 61 7.19 0.651 74 7.57 61 7.35 0.566 GLB 16 .85 30 .80 0.612 16 .88 36 .71 0.087* BIG4 74 .71 61 .73 0.757 74 .71 61 .73 0.757 TQ 74 2.35 61 3.27 0.048** 74 2.04 61 2.72 0.054** LEV 74 .35 61 .32 0.364 74 .33 61 .33 0.963 GDP 74 .03 61 .03 0.688 74 .02 61 .02 0.509 P-score 1 : matching on the propensity score, computed from model (1); Pre Implementation of ERMC : descriptive on the 5 years preceding the implementation of the ERMC. Post Implementation of ERMC : descriptive on the 5 years after the implementation of the ERMC. ERM firms : firms that have implemented an ERM committee up until 2010. Non-ERM firms: firms that have not implemented an ERM committee. P-value (means) : p-value of a (finite sample) t-test for whether the mean difference between companies implementing ERMC and the matching companies is significantly different from zero; : number of matched pairs for which there is information on the row variable. *, ** and ***: Significant difference in levels of the variables at the 10%, 5% and 1% level. logit erm_commit t WDebt_lag betaamr_lag sigma_lag WROA WSize litigation WLiqui gdpgrowth if erm_use_obs>0

38 Table 4b. Descriptive statistics after implementation of ERMC

PAEL B : Matching on observables

Pre Implementation of ERMC Post Implementation of ERMC

ERM-firms oERM firms Test ERM firms oERM firms Test

Variable (firm- Mean (firm- Mean p-value (firm- Mean (firm- Mean p-value year) year) year) year) ERMC 192 1.00 192 0.00 -- 192 1.00 192 0.00 -- rank_em1 157 2,462.3 162 2,689.6 0.163 158 2,379.9 124 3,021.7 0.000*** rank_em1re1 157 2,550.1 162 3,018.4 0.004*** 158 2,437.5 124 3,067.2 0.000*** rank_em3 97 3,452.8 135 3,646.3 0.335 100 3,274.7 99 3,769.1 0.015** AEM1 36 3,596.5 61 3,824.7 0.283 43 3,376.0 49 3,801.7 0.015** AEM2 95 5,236.5 110 4,856.9 0.101 59 5,007.3 56 4,437.9 0.024** TEM1 10 2,805.9 15 3,461.2 0.149 13 2,850.7 13 3,494.3 0.158 TEM2 125 5,310.5 125 5,136.5 0.383 169 5,295.0 133 5,231.8 0.734 EVOL 157 .06 137 .08 0.415 188 .06 140 .07 0.460 EMEA 157 .03 137 .04 0.665 188 .04 140 .06 0.303 ROA 190 -.01 190 -.01 0.786 192 -.00 152 -.06 0.093* LIT 192 .05 190 .07 0.385 192 .05 152 .06 0.774 SIZE 139 7.26 144 7.08 0.441 178 7.34 136 6.87 0.059* GLB 27 .84 44 .77 0.371 35 .86 41 .81 0.415 BIG4 192 .47 191 .60 0.005*** 192 .73 153 .79 0.156 TQ 138 2.59 144 2.58 0.974 178 2.05 136 2.19 0.551 LEV 190 .28 190 .28 0.882 192 .28 152 .29 0.676 GDP 192 .03 190 .03 0.348 192 .02 152 .02 0.977 Matching on observables using EVOL, ROA, Size, LEV and TQ. Pre Implementation of ERMC : descriptive on the 5 years preceding the implementation of the ERMC. Post Implementation of ERMC : descriptive on the 5 years after the implementation of the ERMC. ERM firms : firms that have implemented an ERM committee up until 2010. Non-ERM firms: firms that have not implemented an ERM committee. In the pre implementation columns boldface font identifies the matching variables. P-value : p-value of a (finite sample) t-test for whether the mean difference between companies implementing ERMC and the matching companies is significantly different from zero; : number of matched pairs for which there is information on the row variable. *, ** and ***: Significant difference in levels of the variables at the 10%, 5% and 1% level.

39

Table 5. The impact of implementing ERMC on EM measures: matching estimators

p-score Match Time Mean p- Mean p- Horizon Diff. value Diff. value rank_em1 t+1 571.44* 0.071 149 473.32** 0.019 185 t+2 817.34** 0.014 143 707.97** 0.013 184 t+3 241.38 0.422 156 -163.95 0.589 177 t+4 680.64** 0.034 135 758.48*** 0.008 189 t+5 325.92 0.298 162 366.97 0.186 179 rank_em1re1 t+1 535.70* 0.097 150 305.47 0.285 185 t+2 355.33 0.290 143 349.01 0.235 184 t+3 -161.87 0.597 156 365.14 0.236 178 t+4 443.51 0.181 135 841.00*** 0.003 189 t+5 -44.59 0.885 162 393.09 0.164 179 rank_em3 t+1 170.99 0.656 125 633.95* 0.083 140 t+2 332.96 0.419 107 -76.76 0.824 137 t+3 734.79* 0.080 106 -25,97 0.943 142 t+4 -127.24 0.758 107 746.10* 0.058 134 t+5 540.68 0.158 105 147.76 0.662 129 P-score : matching on the propensity score, computed from model (1); Match : matching on observables using EVOL, ROA, Size, LEV and TQ. Mean Diff. : diff-in-diff estimator for time horizon t+j (controlled – treated); p-value : p-value of the (finite sample) t-test for significant diff-in-diff between companies initiating CSR disclosure and the matching companies for time horizon t+j; : number of matched pairs with information on the outcome variable. *, ** and ***: Significant difference in differences at the 10%, 5% and 1% level.

40 Table 5 (cont.) The impact of implementing ERMC on Aggregated EM measures: matching estimators

p-score 2 Match Time Mean p- Mean p- Horizon Diff. value Diff. value AEM1 t+1 45.54 0.870 75 203.69 0.571 52 t+2 109.72 0.744 59 9.878 0.976 48 t+3 -52.45 0.862 68 -54.14 0.863 55 t+4 173.23 0.522 65 243.16 0.460 53 t+5 120.77 0.648 73 243.58 0.501 42 AEM2 t+1 -658.63** 0.014 209 -92.33 0.757 158 t+2 -399.42 0.165 199 -385.71 0.204 160 t+3 -161.19 0.554 207 -68.47 0.814 165 t+4 -371.38 0.142 211 -112.00 0.704 162 t+5 -12.15 0.963 216 -416.36 0.151 149 TEM1 t+1 -322.04 0.554 25 1,188.19* 0.083 18 t+2 -577.52 0.406 15 633.89 0.207 18 t+3 -728.47 0.107 23 613.21 0.309 20 t+4 -825.60** 0.034 24 959.03* 0.095 18 t+5 -859.21** 0.045 19 448.48 0.596 14 TEM2 t+1 -271.49 0.360 169 -6.47 0.977 252 t+2 -218.93 0.462 154 -159.82 0.482 247 t+3 -673.03** 0.021 144 -140.64 0.542 243 t+4 -146.55 0.577 153 -304.28 0.183 243 t+5 38.74 0.884 161 -386.54 0.105 216 P-score : matching on the propensity score, computed from model (1); Match : matching on observables using EVOL ROA, Size, LEV and TQ. Mean Diff. : diff-in-diff estimator for time horizon t+j (controlled – treated); p-value : p-value of the (finite sample) t-test for significant diff-in- diff between companies initiating CSR disclosure and the matching companies for time horizon t+j; : number of matched pairs with information on the outcome variable. *, ** and ***: Significant difference in differences at the 10%, 5% and 1% level.

41 Table 6. Relationship between EM and lagged ERMC

Dependent variable: EM variables

Rank_em Rank_em1re Rank_em AEM1 AEM2 TEM1 TEM2 1 1 3 ERMC -42.58 -221.03* -841.73* -355.5* -515.93** -567.73** -476.69*** . L1 (-0.30) (1.66) (1.78) (-1.90) (-2.54) (-2.41) (-3.07) -18.14 -7.03 19.09 123.28 314.88*** 61.27** 304.49*** ROA (-0.16) (-0.16) (0.15) (1.74) (3.27) (2.44) (3.45) .00 .00 -.01 -7.40 -3.41 2.92 -3.28 TQ (0.08) (0.89) (-0.80) (-2.75) (-0.88) (-0.46) (-0.58) 100.13** -94.16 -50.92 208.94* -70.49 -39.81 -56.28 LEV * (-0.42) (-0.38) (1.85) (-0.69) (-0.31) (-1.17) (-3.68) 10.15 4.91 -35.51 -78.06 -159.07*** 55.12 -124.07* SIZE (0.36) (-0.39) (-1.06) (-3.34) (-2.87) (-1.26) (-1.94) -86.71 -37.06 63.49 -69.68 -268.73*** 121.25 -165.84 BIG4 (-0.93) (-0.39) (0.63) (-1.31) (-3.12) (-1.50) (-1.36) 4250.4** 13246.4** 1867.0** 12560.4** 938.38*** -63.15 1140.8 GPD * * * * (-2.78) (-0.41) (1.04) (4.07) (3.93) (4.10) (4.76) Firm Yes Yes Yes Yes Yes Yes Yes effects N 10,625 10,630 8.222 6,103 19,043 5,079 18,414 R- 0.000 0.000 0.001 0.010 0.040 0.05 0.047 quare Fixed effects panel regression; t-stats (in parentheses) are based on standard errors clustered by firm and year; *, ** and *** denote significance at the 10%, 5%, and 1% level based on a two-sided test. Column headings denote the specific EM measures used as regressors in each specification. L1 denotes one-period lagged values of the corresponding regressor. Time is a linear time trend. Adjusted R2 are within R2, computed for the firm-demeaned data.

42

Table 8. The impact of implementing ERMC on Performance (ROA), earnings volatility (EVOL) and earnings mean (EMEA): matching estimators

p-score 2 Match Time Mean p- Mean p- Horizon Diff. value Diff. value ROA t+1 -.05 0.204 274 -.01 0.617 379 t+2 -.04 0.399 269 -.04 0.261 371 t+3 -.09* 0.076 269 -.05 0.295 370 t+4 -.06** 0.037 269 -.02 0.246 373 t+5 -.04 0.425 273 -.15*** 0.003 357 EVOL t+1 .05 0.238 142 .01 0.715 285 t+2 .04 0.298 136 0.1 0.275 283 t+3 .06** 0.019 138 .03** 0.045 276 t+4 .02 0.503 143 .03* 0.052 262 t+5 .06** 0.039 133 .04*** 0.000 232 EMEA t+1 -.04 0.388 142 .02 0.284 285 t+2 -.06 0.352 136 .02 0.267 283 t+3 -.06 0.147 138 .01 0.692 276 t+4 -.03 0.548 143 .01 0.532 262 t+5 -.07 0.154 144 -.00 .986 232 P-score : matching on the propensity score, computed from model (1); Match : matching on observables using EVOL EMEAN, ROA, Size, LEV and TQ. Mean Diff. : diff-in-diff estimator for time horizon t+j (controlled – treated); p-value : p- value of the (finite sample) t-test for significant diff-in-diff between ERM companies and the matching companies for time horizon t+j; : number of matched pairs. *, ** and ***: Significant difference in differences at the 10%, 5% and 1% level.

43 Table 8. Relationship between Earnings Volatility and ERMC

Dependent variable: EVOL Dependent variable: EMEA Basic TIME Lagged Basic TIME Lagged Model effect Model effect TIME .00099** .00100** .00111 .00111 (2.09) (2.09) (1.07) (1.07) ERMC -.00231 -.00570** -.00670*** -.00080 -.00458 -.00563 (-1.44) (-2.35) (-2.74) (-0.16) (-0.75) (-0.96) ROA -.00000*** .00000*** -.00000*** .00000** .00000*** .00000** (-3.17) (-3.15) (-3.15) (2.33) (2.35) (2.35) TQ -.00000*** -.00000*** -.00000*** .00000*** .00000*** .00000 (-6.65) (-7.36) (-7.42) (3.35) (2.61) (2.63) LEV .00003 .00002 .00002 -.00002 -.00002 -.00002 (0.94) (0.92) (0.92) (-0.70) (-0.75) (-0.75) SIZE .00516*** .00457*** .00457*** .00023 -.00043 -.00043 (4.15) (4.06) (4.06) (0.11) (-0.24) (-0.24) GPD -.19465*** -.09320 -.09311 -.07440 .03867 .03874 (-2.72) (-1.39) (-1.39) (-.082) (0.28) (0.28) Constant .23922*** .22616*** .22612*** .12993*** .14449*** -.14452*** (36.39) (20.16) (20.14) (11.29) (6.60) (-6.60) N 68496 68496 68496 68496 68496 68496 R-quare 0.0042 0.0049 0.0049 0.1047 0.1047 0.0010 Fixed effects panel regression; t-stats (in parentheses) are based on standard errors clustered by firm and year; *, ** and *** denote significance at the 10%, 5%, and 1% level based on a two-sided test. Column headings denote the specific EM measures used as regressors in each specification. L1 denotes one-period lagged values of the corresponding regressor. Time is a linear time trend. Adjusted R2 are within R2, computed for the firm-demeaned data.

44 Table 9. Effect of ERMC on EM in Low Earnings Volatility and Low Earnings Mean companies

Dependent variable: EM measures AEM1 AEM2 TEM1 TEM2 ERMC -228.13 * -433.90* -518.16** -388.86** (-1.64) (-1.82) (-2.27) (-1.86) L_Vol 74.67*** 2.24 140.63*** 58.76* (2.59) (007) (2.69) (1.68) ERMC_LV -285.84 -122.13 -874.55*** -250.45** (-1.14) (-0.67) (-2.81) (-2.35) L_Mean -8.70 4.22 29.87 -45.12 (-0.23) (0.04) (0.41) (-0.46) ERMC_LM 543.82 44.54 249.98 176.71 (1.61) (0.13) (0.48) (0.48) ROA 17.72 110.84*** 11.90 16.52 (1.21) (6.97) (0.64) (0.97) TQ -.59 -1.96 -1.75 -2.89** (-0.55) (-1.40) (-1.18) (-2.05) LEV -32.82 -149.88*** -131.64*** -96.42*** (-1.55) (-4.25) (-3.47) (-3.66) SIZE -27.55 ** -108.08*** -56.44*** -78.47 ** (-3.35) (-4.01) (-3.23) (-3.05) GPD 2628*** 10094*** 5842*** 9643 *** (3.67) (3.72) (3.50) (4.09) N 31,522 75,770 14,829 70,899 R-quare 0.0029 0.0193 0.0198 0.0238 Fixed effects panel regression; t-stats (in parentheses) are based on standard errors clustered by firm and year; *, ** and *** denote significance at the 10%, 5%, and 1% level based on a two-sided test. Column headings denote the specific EM measures used as regressors in each specification. Time is a linear time trend. Adjusted R 2 are within R2, computed for the firm-demeaned data.

45 Table 10. Effect of ERMC on EM in High Earnings Volatility and High Earnings Mean companies

Dependent variable: EM measures AEM1 AEM2 TEM1 TEM2 ERMC -238.39** -489.31*** -715.70** -536.37*** (-2.42) (-2.61) (-2.33) (-2.93) H_Vol 13.29 39.28 -39.84 52.39 (0.61) (0.67) (-0.49) (0.75) ERMC_HV 604.16*** 193.69 182.27 525.00*** (2.96) (0.60) (0.83) (2.75) H_Mean 35.37 116.51 37.36 127.85 (1.07) (1.02) (0.49) (1.16) ERMC_HM -324.59 -49.35 265.66 -69.11 (-1.50) (-0.22) (0.64) (-0.42) ROA 18.57 -114.23*** 15.72 18.55 (1.25) (6.99) (0.83) (1.08) TQ -.63 -2.04 -1.79 -2.91** (-0.58) (-1.44) (-1.24) (-2.07) LEV -30.81 -141.69*** -126.02*** -93.03*** (-1.45) (-3.93) (-3.32) (-3.60) SIZE -27.69*** -109.75*** -58.90*** -79.61*** (-3.31) (-4.08) (-3.25) (3.12) GPD 2488*** 9680*** 5861*** 9317*** (3.33) (3.53) (3.35) (3.85) N 31,522 75,770 14,829 70,899 R-quare 0.0029 0.0199 0.0192 0.0249 Fixed effects panel regression; t-stats (in parentheses) are based on standard errors clustered by firm and year; *, ** and *** denote significance at the 10%, 5%, and 1% level based on a two-sided test. Column headings denote the specific EM measures used as regressors in each specification. Time is a linear time trend. Adjusted R 2 are within R2, computed for the firm-demeaned data.

46